Interpretive Summary: Cotton yield variability within a field can be caused by multiple factors such as soil property variability, water management, and fertilizer application. To study effects of various factors on yield, airborne multispectral imaging has been used to provide data and information in a timely and cost-effective fashion. The scientists at USDA-ARS Crop production Systems Research Unit conducted a field study on a 10-ha cotton field with six nitrogen application rates under irrigated and non-irrigated environment. Multispectral images on the crop canopy over the field were acquired using a high-performance multispectral camera in an agricultural airplane. The images were processed and analyzed to develop models for yield estimation. Results indicated that the information extracted from the images had a close relationship with yield, and the model of yield with image information and soil electrical conductivity measurement could estimate the yield well. This study indicates that aerial multispectral remote sensing is promising in estimating cotton yield variation under different irrigation and nitrogen treatment.

Technical Abstract:
Cotton yield varied spatially within a field. The variability can be caused by various production inputs such as soil property, water management, and fertilizer application. Airborne multispectral imaging is capable of providing data and information to study effects of the inputs on the yield qualitatively and quantitatively in a timely and cost-effective fashion. A 10-ha cotton field with irrigation and non-irrigation 2x2 blocks was used in this study. Six nitrogen application treatments were randomized with 2 replications within each block. As plant canopy was closed, airborne multispectral images of the field were acquired using a 3-CCD MS4100 camera. The images were processed to generate various vegetation indices. The vegetation indices were evaluated for the best performance to characterize the yield. The effect of irrigation on vegetation indices was significant. Models for yield estimation were developed and verified by comparing the estimated and actual yields. Results indicated that ratio of vegetation index (RVI) had a close relationship with yield (R2=0.47). Better yield estimation could be obtained using a model with RVI and soil EC (electrical conductivity) measurements of the field as explanatory variables (R2=0.53). This research demonstrated the capability of aerial multispectral remote sensing in estimating cotton yield variation, and provided the methods for similar studies.